Overview

Dataset statistics

Number of variables38
Number of observations23175
Missing cells84711
Missing cells (%)9.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.1 MiB
Average record size in memory1.4 KiB

Variable types

Categorical21
Numeric10
Unsupported7

Alerts

Ano has constant value "2015" Constant
Codigo SGIF has a high cardinality: 23175 distinct values High cardinality
Concelho has a high cardinality: 319 distinct values High cardinality
Freguesia has a high cardinality: 3290 distinct values High cardinality
DataAlerta has a high cardinality: 346 distinct values High cardinality
DataExtincao has a high cardinality: 349 distinct values High cardinality
Data1Intervencao has a high cardinality: 346 distinct values High cardinality
NUT has a high cardinality: 3861 distinct values High cardinality
Perimetro has a high cardinality: 87 distinct values High cardinality
Codigo_ANPC is highly correlated with Distrito and 8 other fieldsHigh correlation
INE is highly correlated with Codigo_ANPC and 8 other fieldsHigh correlation
AA_Mato (ha) is highly correlated with AA_EspacosFlorestais (pov+mato)(ha) and 1 other fieldsHigh correlation
AA_Agricola (ha) is highly correlated with AgricolaHigh correlation
AA_EspacosFlorestais (pov+mato)(ha) is highly correlated with AA_Povoamento (ha) and 3 other fieldsHigh correlation
AA_Total (pov+mato+agric) (ha) is highly correlated with AA_Povoamento (ha) and 3 other fieldsHigh correlation
Falso Alarme is highly correlated with Tipo and 1 other fieldsHigh correlation
Incendio is highly correlated with FogachoHigh correlation
Agricola is highly correlated with Tipo and 2 other fieldsHigh correlation
AA_Povoamento (ha) is highly correlated with AA_EspacosFlorestais (pov+mato)(ha) and 2 other fieldsHigh correlation
UGF is highly correlated with Codigo_ANPC and 10 other fieldsHigh correlation
Região PROF is highly correlated with Codigo_ANPC and 7 other fieldsHigh correlation
Queimada is highly correlated with Tipo and 1 other fieldsHigh correlation
Distrito is highly correlated with Codigo_ANPC and 10 other fieldsHigh correlation
Tipo is highly correlated with Distrito and 7 other fieldsHigh correlation
APS is highly correlated with Codigo_ANPC and 10 other fieldsHigh correlation
Reacendimentos is highly correlated with Causa and 1 other fieldsHigh correlation
Ano is highly correlated with UGF and 12 other fieldsHigh correlation
Perimetro is highly correlated with Codigo_ANPC and 15 other fieldsHigh correlation
TipoCausa is highly correlated with Distrito and 5 other fieldsHigh correlation
Fogacho is highly correlated with Tipo and 3 other fieldsHigh correlation
x is highly correlated with Codigo_ANPC and 7 other fieldsHigh correlation
y is highly correlated with Codigo_ANPC and 7 other fieldsHigh correlation
Causa is highly correlated with Codigo_ANPC and 7 other fieldsHigh correlation
DataExtincao has 312 (1.3%) missing values Missing
HoraExtincao has 317 (1.4%) missing values Missing
Data1Intervencao has 1199 (5.2%) missing values Missing
Hora1Intervencao has 1213 (5.2%) missing values Missing
FonteAlerta has 23175 (100.0%) missing values Missing
Perimetro has 22349 (96.4%) missing values Missing
APS has 22116 (95.4%) missing values Missing
Causa has 7013 (30.3%) missing values Missing
TipoCausa has 7013 (30.3%) missing values Missing
AA_Povoamento (ha) is highly skewed (γ1 = 46.40516403) Skewed
AA_Mato (ha) is highly skewed (γ1 = 80.19057206) Skewed
AA_Agricola (ha) is highly skewed (γ1 = 42.94837127) Skewed
AA_EspacosFlorestais (pov+mato)(ha) is highly skewed (γ1 = 58.04817374) Skewed
AA_Total (pov+mato+agric) (ha) is highly skewed (γ1 = 56.72923777) Skewed
Codigo SGIF is uniformly distributed Uniform
Codigo SGIF has unique values Unique
Codigo_ANPC has unique values Unique
Local is an unsupported type, check if it needs cleaning or further analysis Unsupported
lat is an unsupported type, check if it needs cleaning or further analysis Unsupported
lon is an unsupported type, check if it needs cleaning or further analysis Unsupported
HoraAlerta is an unsupported type, check if it needs cleaning or further analysis Unsupported
HoraExtincao is an unsupported type, check if it needs cleaning or further analysis Unsupported
Hora1Intervencao is an unsupported type, check if it needs cleaning or further analysis Unsupported
FonteAlerta is an unsupported type, check if it needs cleaning or further analysis Unsupported
AA_Povoamento (ha) has 18165 (78.4%) zeros Zeros
AA_Mato (ha) has 9884 (42.6%) zeros Zeros
AA_Agricola (ha) has 19012 (82.0%) zeros Zeros
AA_EspacosFlorestais (pov+mato)(ha) has 6824 (29.4%) zeros Zeros
AA_Total (pov+mato+agric) (ha) has 2925 (12.6%) zeros Zeros

Reproduction

Analysis started2022-09-22 17:27:24.648386
Analysis finished2022-09-22 17:27:40.494837
Duration15.85 seconds
Software versionpandas-profiling v3.3.1
Download configurationconfig.json

Variables

Ano
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2015
23175 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters92700
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
201523175
100.0%

Length

2022-09-22T18:27:40.529483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T18:27:40.587863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
201523175
100.0%

Most occurring characters

ValueCountFrequency (%)
223175
25.0%
023175
25.0%
123175
25.0%
523175
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number92700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
223175
25.0%
023175
25.0%
123175
25.0%
523175
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common92700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
223175
25.0%
023175
25.0%
123175
25.0%
523175
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII92700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
223175
25.0%
023175
25.0%
123175
25.0%
523175
25.0%

Codigo SGIF
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct23175
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
DM315252
 
1
DM3151467
 
1
DM2154174
 
1
DM2154168
 
1
BL415734
 
1
Other values (23170)
23170 

Length

Max length9
Median length8
Mean length8.317281553
Min length6

Characters and Unicode

Total characters192753
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23175 ?
Unique (%)100.0%

Sample

1st rowDM315252
2nd rowDM215305
3rd rowDM415293
4th rowDM315261
5th rowBL115321

Common Values

ValueCountFrequency (%)
DM3152521
 
< 0.1%
DM31514671
 
< 0.1%
DM21541741
 
< 0.1%
DM21541681
 
< 0.1%
BL4157341
 
< 0.1%
DM21541651
 
< 0.1%
RO11511191
 
< 0.1%
DM21541631
 
< 0.1%
DM21520931
 
< 0.1%
BI2154741
 
< 0.1%
Other values (23165)23165
> 99.9%

Length

2022-09-22T18:27:40.638969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dm3152521
 
< 0.1%
dm4152931
 
< 0.1%
bl1153211
 
< 0.1%
dm4152891
 
< 0.1%
tm1153821
 
< 0.1%
bl215921
 
< 0.1%
dm2153341
 
< 0.1%
tm1153851
 
< 0.1%
dm4153101
 
< 0.1%
tm1153881
 
< 0.1%
Other values (23165)23165
> 99.9%

Most occurring characters

ValueCountFrequency (%)
142871
22.2%
529984
15.6%
218363
9.5%
313384
 
6.9%
410932
 
5.7%
M10446
 
5.4%
D8231
 
4.3%
66579
 
3.4%
76346
 
3.3%
B6311
 
3.3%
Other values (10)39306
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number146403
76.0%
Uppercase Letter46350
 
24.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
142871
29.3%
529984
20.5%
218363
12.5%
313384
 
9.1%
410932
 
7.5%
66579
 
4.5%
76346
 
4.3%
86038
 
4.1%
95981
 
4.1%
05925
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
M10446
22.5%
D8231
17.8%
B6311
13.6%
L4957
10.7%
R4727
10.2%
O4727
10.2%
T3322
 
7.2%
A1691
 
3.6%
I1354
 
2.9%
G584
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common146403
76.0%
Latin46350
 
24.0%

Most frequent character per script

Common
ValueCountFrequency (%)
142871
29.3%
529984
20.5%
218363
12.5%
313384
 
9.1%
410932
 
7.5%
66579
 
4.5%
76346
 
4.3%
86038
 
4.1%
95981
 
4.1%
05925
 
4.0%
Latin
ValueCountFrequency (%)
M10446
22.5%
D8231
17.8%
B6311
13.6%
L4957
10.7%
R4727
10.2%
O4727
10.2%
T3322
 
7.2%
A1691
 
3.6%
I1354
 
2.9%
G584
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII192753
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
142871
22.2%
529984
15.6%
218363
9.5%
313384
 
6.9%
410932
 
5.7%
M10446
 
5.4%
D8231
 
4.3%
66579
 
3.4%
76346
 
3.3%
B6311
 
3.3%
Other values (10)39306
20.4%

Codigo_ANPC
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23175
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.015104911 × 1012
Minimum2.015010001 × 1012
Maximum2.015180069 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size181.2 KiB
2022-09-22T18:27:40.709410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.015010001 × 1012
5-th percentile2.015010055 × 1012
Q12.015060012 × 1012
median2.015120017 × 1012
Q32.01514006 × 1012
95-th percentile2.015180017 × 1012
Maximum2.015180069 × 1012
Range170067470
Interquartile range (IQR)80047573

Descriptive statistics

Standard deviation53145771.6
Coefficient of variation (CV)2.637369961 × 10-5
Kurtosis-1.0689232
Mean2.015104911 × 1012
Median Absolute Deviation (MAD)39989211
Skewness-0.4462520891
Sum4.670005631 × 1016
Variance2.824473038 × 1015
MonotonicityNot monotonic
2022-09-22T18:27:40.784294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.015160008 × 10121
 
< 0.1%
2.015160024 × 10121
 
< 0.1%
2.015130133 × 10121
 
< 0.1%
2.015130133 × 10121
 
< 0.1%
2.015100046 × 10121
 
< 0.1%
2.015130133 × 10121
 
< 0.1%
2.015140051 × 10121
 
< 0.1%
2.015130133 × 10121
 
< 0.1%
2.015130101 × 10121
 
< 0.1%
2.015050025 × 10121
 
< 0.1%
Other values (23165)23165
> 99.9%
ValueCountFrequency (%)
2.015010001 × 10121
< 0.1%
2.015010001 × 10121
< 0.1%
2.015010001 × 10121
< 0.1%
2.015010001 × 10121
< 0.1%
2.015010002 × 10121
< 0.1%
2.015010002 × 10121
< 0.1%
2.015010002 × 10121
< 0.1%
2.015010002 × 10121
< 0.1%
2.015010002 × 10121
< 0.1%
2.015010002 × 10121
< 0.1%
ValueCountFrequency (%)
2.015180069 × 10121
< 0.1%
2.015180069 × 10121
< 0.1%
2.015180069 × 10121
< 0.1%
2.015180068 × 10121
< 0.1%
2.015180068 × 10121
< 0.1%
2.015180067 × 10121
< 0.1%
2.015180067 × 10121
< 0.1%
2.015180067 × 10121
< 0.1%
2.015180067 × 10121
< 0.1%
2.015180067 × 10121
< 0.1%

Tipo
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Florestal
15464 
Agrícola
4163 
Falso Alarme
2925 
Queimada
 
623

Length

Max length12
Median length9
Mean length9.172125135
Min length8

Characters and Unicode

Total characters212564
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFlorestal
2nd rowFlorestal
3rd rowFlorestal
4th rowFalso Alarme
5th rowAgrícola

Common Values

ValueCountFrequency (%)
Florestal15464
66.7%
Agrícola4163
 
18.0%
Falso Alarme2925
 
12.6%
Queimada623
 
2.7%

Length

2022-09-22T18:27:40.861517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T18:27:40.927541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
florestal15464
59.2%
agrícola4163
 
16.0%
falso2925
 
11.2%
alarme2925
 
11.2%
queimada623
 
2.4%

Most occurring characters

ValueCountFrequency (%)
l40941
19.3%
a26723
12.6%
o22552
10.6%
r22552
10.6%
e19012
8.9%
F18389
8.7%
s18389
8.7%
t15464
 
7.3%
A7088
 
3.3%
c4163
 
2.0%
Other values (8)17291
8.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter183539
86.3%
Uppercase Letter26100
 
12.3%
Space Separator2925
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l40941
22.3%
a26723
14.6%
o22552
12.3%
r22552
12.3%
e19012
10.4%
s18389
10.0%
t15464
 
8.4%
c4163
 
2.3%
g4163
 
2.3%
í4163
 
2.3%
Other values (4)5417
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
F18389
70.5%
A7088
 
27.2%
Q623
 
2.4%
Space Separator
ValueCountFrequency (%)
2925
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin209639
98.6%
Common2925
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l40941
19.5%
a26723
12.7%
o22552
10.8%
r22552
10.8%
e19012
9.1%
F18389
8.8%
s18389
8.8%
t15464
 
7.4%
A7088
 
3.4%
c4163
 
2.0%
Other values (7)14366
 
6.9%
Common
ValueCountFrequency (%)
2925
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII208401
98.0%
None4163
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l40941
19.6%
a26723
12.8%
o22552
10.8%
r22552
10.8%
e19012
9.1%
F18389
8.8%
s18389
8.8%
t15464
 
7.4%
A7088
 
3.4%
c4163
 
2.0%
Other values (7)13128
 
6.3%
None
ValueCountFrequency (%)
í4163
100.0%

Distrito
Categorical

HIGH CORRELATION

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Porto
4444 
Braga
2231 
Lisboa
2225 
Aveiro
1774 
Viseu
1528 
Other values (14)
10973 

Length

Max length16
Median length14
Mean length6.92030205
Min length4

Characters and Unicode

Total characters160378
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowViana do Castelo
2nd rowPorto
3rd rowBraga
4th rowViana do Castelo
5th rowViseu

Common Values

ValueCountFrequency (%)
Porto4444
19.2%
Braga2231
9.6%
Lisboa2225
9.6%
Aveiro1774
 
7.7%
Viseu1528
 
6.6%
Viana do Castelo1483
 
6.4%
Vila Real1481
 
6.4%
Santarém1328
 
5.7%
Setúbal1173
 
5.1%
Leiria906
 
3.9%
Other values (9)4602
19.9%

Length

2022-09-22T18:27:40.988033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
porto4444
15.7%
braga2231
 
7.9%
lisboa2225
 
7.9%
castelo2103
 
7.4%
aveiro1774
 
6.3%
viana1559
 
5.5%
do1559
 
5.5%
viseu1528
 
5.4%
vila1481
 
5.2%
real1481
 
5.2%
Other values (11)7933
28.0%

Most occurring characters

ValueCountFrequency (%)
a26406
16.5%
o19095
11.9%
r15134
 
9.4%
i11128
 
6.9%
e10129
 
6.3%
t9411
 
5.9%
l6601
 
4.1%
s5856
 
3.7%
5143
 
3.2%
P4807
 
3.0%
Other values (23)46668
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter128400
80.1%
Uppercase Letter26835
 
16.7%
Space Separator5143
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a26406
20.6%
o19095
14.9%
r15134
11.8%
i11128
8.7%
e10129
 
7.9%
t9411
 
7.3%
l6601
 
5.1%
s5856
 
4.6%
n4164
 
3.2%
b4147
 
3.2%
Other values (10)16329
12.7%
Uppercase Letter
ValueCountFrequency (%)
P4807
17.9%
V4568
17.0%
B3946
14.7%
L3131
11.7%
C2852
10.6%
S2501
9.3%
A1774
 
6.6%
R1481
 
5.5%
G809
 
3.0%
F584
 
2.2%
Other values (2)382
 
1.4%
Space Separator
ValueCountFrequency (%)
5143
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin155235
96.8%
Common5143
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a26406
17.0%
o19095
12.3%
r15134
 
9.7%
i11128
 
7.2%
e10129
 
6.5%
t9411
 
6.1%
l6601
 
4.3%
s5856
 
3.8%
P4807
 
3.1%
V4568
 
2.9%
Other values (22)42100
27.1%
Common
ValueCountFrequency (%)
5143
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII156838
97.8%
None3540
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a26406
16.8%
o19095
12.2%
r15134
 
9.6%
i11128
 
7.1%
e10129
 
6.5%
t9411
 
6.0%
l6601
 
4.2%
s5856
 
3.7%
5143
 
3.3%
P4807
 
3.1%
Other values (19)43128
27.5%
None
ValueCountFrequency (%)
é1328
37.5%
ú1173
33.1%
ç733
20.7%
É306
 
8.6%

Concelho
Categorical

HIGH CARDINALITY

Distinct319
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Penafiel
 
477
Paredes
 
437
Sintra
 
405
Gondomar
 
386
Santa Maria da Feira
 
365
Other values (314)
21105 

Length

Max length27
Median length22
Mean length10.10015102
Min length4

Characters and Unicode

Total characters234071
Distinct characters61
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.1%

Sample

1st rowPonte de Lima
2nd rowMarco de Canaveses
3rd rowVila Verde
4th rowPonte de Lima
5th rowCastro Daire

Common Values

ValueCountFrequency (%)
Penafiel477
 
2.1%
Paredes437
 
1.9%
Sintra405
 
1.7%
Gondomar386
 
1.7%
Santa Maria da Feira365
 
1.6%
Vila Nova de Gaia355
 
1.5%
Amarante353
 
1.5%
Montalegre347
 
1.5%
Santo Tirso345
 
1.5%
Guimarães342
 
1.5%
Other values (309)19363
83.6%

Length

2022-09-22T18:27:41.055514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de4091
 
10.4%
vila1543
 
3.9%
do1091
 
2.8%
da1015
 
2.6%
nova700
 
1.8%
ponte523
 
1.3%
castelo517
 
1.3%
paredes504
 
1.3%
penafiel477
 
1.2%
santa411
 
1.0%
Other values (322)28325
72.3%

Most occurring characters

ValueCountFrequency (%)
a34092
14.6%
e22528
 
9.6%
o18121
 
7.7%
r16472
 
7.0%
16022
 
6.8%
i14634
 
6.3%
d10658
 
4.6%
s9712
 
4.1%
n9535
 
4.1%
l9469
 
4.0%
Other values (51)72828
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter182649
78.0%
Uppercase Letter34884
 
14.9%
Space Separator16022
 
6.8%
Dash Punctuation516
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a34092
18.7%
e22528
12.3%
o18121
9.9%
r16472
9.0%
i14634
8.0%
d10658
 
5.8%
s9712
 
5.3%
n9535
 
5.2%
l9469
 
5.2%
t5698
 
3.1%
Other values (23)31730
17.4%
Uppercase Letter
ValueCountFrequency (%)
V4353
12.5%
C3558
10.2%
A3510
10.1%
M3483
10.0%
P3309
9.5%
S2890
8.3%
F2138
 
6.1%
B2037
 
5.8%
L1791
 
5.1%
G1473
 
4.2%
Other values (16)6342
18.2%
Space Separator
ValueCountFrequency (%)
16022
100.0%
Dash Punctuation
ValueCountFrequency (%)
-516
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin217533
92.9%
Common16538
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a34092
15.7%
e22528
 
10.4%
o18121
 
8.3%
r16472
 
7.6%
i14634
 
6.7%
d10658
 
4.9%
s9712
 
4.5%
n9535
 
4.4%
l9469
 
4.4%
t5698
 
2.6%
Other values (49)66614
30.6%
Common
ValueCountFrequency (%)
16022
96.9%
-516
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII228670
97.7%
None5401
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a34092
14.9%
e22528
 
9.9%
o18121
 
7.9%
r16472
 
7.2%
16022
 
7.0%
i14634
 
6.4%
d10658
 
4.7%
s9712
 
4.2%
n9535
 
4.2%
l9469
 
4.1%
Other values (36)67427
29.5%
None
ValueCountFrequency (%)
ã1995
36.9%
ç1163
21.5%
é730
 
13.5%
ó573
 
10.6%
á164
 
3.0%
â121
 
2.2%
ú106
 
2.0%
Á98
 
1.8%
à95
 
1.8%
ê80
 
1.5%
Other values (5)276
 
5.1%

Freguesia
Categorical

HIGH CARDINALITY

Distinct3290
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Lordelo
 
113
São Pedro da Cova
 
87
Telões
 
84
Vilela
 
74
Moita
 
66
Other values (3285)
22751 

Length

Max length127
Median length85
Mean length12.41001079
Min length2

Characters and Unicode

Total characters287602
Distinct characters70
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique642 ?
Unique (%)2.8%

Sample

1st rowSerdedelo
2nd rowVila Boa de Quires
3rd rowLage
4th rowBoalhosa
5th rowCastro Daire

Common Values

ValueCountFrequency (%)
Lordelo113
 
0.5%
São Pedro da Cova87
 
0.4%
Telões84
 
0.4%
Vilela74
 
0.3%
Moita66
 
0.3%
São Salvador64
 
0.3%
Nossa Senhora de Fátima61
 
0.3%
Cabril61
 
0.3%
Canelas60
 
0.3%
São Cosme57
 
0.2%
Other values (3280)22448
96.9%

Length

2022-09-22T18:27:41.137052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de3534
 
7.5%
são2053
 
4.4%
do1206
 
2.6%
e1165
 
2.5%
das1109
 
2.4%
da976
 
2.1%
freguesias969
 
2.1%
união969
 
2.1%
santa802
 
1.7%
vila744
 
1.6%
Other values (2661)33575
71.3%

Most occurring characters

ValueCountFrequency (%)
a35537
 
12.4%
e26675
 
9.3%
o25810
 
9.0%
23928
 
8.3%
r19460
 
6.8%
i18127
 
6.3%
s14630
 
5.1%
d13444
 
4.7%
n11174
 
3.9%
l9547
 
3.3%
Other values (60)89270
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter222774
77.5%
Uppercase Letter38271
 
13.3%
Space Separator23928
 
8.3%
Open Punctuation840
 
0.3%
Close Punctuation840
 
0.3%
Dash Punctuation428
 
0.1%
Other Punctuation427
 
0.1%
Connector Punctuation94
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a35537
16.0%
e26675
12.0%
o25810
11.6%
r19460
8.7%
i18127
8.1%
s14630
 
6.6%
d13444
 
6.0%
n11174
 
5.0%
l9547
 
4.3%
t6861
 
3.1%
Other values (25)41509
18.6%
Uppercase Letter
ValueCountFrequency (%)
S6249
16.3%
C4514
11.8%
M3658
9.6%
A3254
8.5%
P2835
 
7.4%
V2783
 
7.3%
B2153
 
5.6%
R1783
 
4.7%
F1782
 
4.7%
L1486
 
3.9%
Other values (18)7774
20.3%
Other Punctuation
ValueCountFrequency (%)
,425
99.5%
/2
 
0.5%
Space Separator
ValueCountFrequency (%)
23928
100.0%
Open Punctuation
ValueCountFrequency (%)
(840
100.0%
Close Punctuation
ValueCountFrequency (%)
)840
100.0%
Dash Punctuation
ValueCountFrequency (%)
-428
100.0%
Connector Punctuation
ValueCountFrequency (%)
_94
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin261045
90.8%
Common26557
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a35537
13.6%
e26675
 
10.2%
o25810
 
9.9%
r19460
 
7.5%
i18127
 
6.9%
s14630
 
5.6%
d13444
 
5.2%
n11174
 
4.3%
l9547
 
3.7%
t6861
 
2.6%
Other values (53)79780
30.6%
Common
ValueCountFrequency (%)
23928
90.1%
(840
 
3.2%
)840
 
3.2%
-428
 
1.6%
,425
 
1.6%
_94
 
0.4%
/2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII278203
96.7%
None9399
 
3.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a35537
12.8%
e26675
 
9.6%
o25810
 
9.3%
23928
 
8.6%
r19460
 
7.0%
i18127
 
6.5%
s14630
 
5.3%
d13444
 
4.8%
n11174
 
4.0%
l9547
 
3.4%
Other values (43)79871
28.7%
None
ValueCountFrequency (%)
ã5413
57.6%
ç1002
 
10.7%
ó766
 
8.1%
é524
 
5.6%
á475
 
5.1%
õ420
 
4.5%
ê179
 
1.9%
í142
 
1.5%
â141
 
1.5%
ô98
 
1.0%
Other values (7)239
 
2.5%

Local
Unsupported

REJECTED
UNSUPPORTED

Missing1
Missing (%)< 0.1%
Memory size1.8 MiB

INE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3599
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105733.6689
Minimum10101
Maximum182413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size181.2 KiB
2022-09-22T18:27:41.215438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10101
5-th percentile11004
Q160206
median121303
Q3142111
95-th percentile180413
Maximum182413
Range172312
Interquartile range (IQR)81905

Descriptive statistics

Standard deviation53162.19047
Coefficient of variation (CV)0.502793396
Kurtosis-1.069403339
Mean105733.6689
Median Absolute Deviation (MAD)38902
Skewness-0.4443551614
Sum2450377777
Variance2826218495
MonotonicityNot monotonic
2022-09-22T18:27:41.287811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13041087
 
0.4%
13101386
 
0.4%
13040971
 
0.3%
15060362
 
0.3%
13171557
 
0.2%
17060156
 
0.2%
15070254
 
0.2%
1051451
 
0.2%
15080451
 
0.2%
13013550
 
0.2%
Other values (3589)22550
97.3%
ValueCountFrequency (%)
101014
 
< 0.1%
101022
 
< 0.1%
101031
 
< 0.1%
1010414
0.1%
101055
 
< 0.1%
101073
 
< 0.1%
101083
 
< 0.1%
101094
 
< 0.1%
101102
 
< 0.1%
101111
 
< 0.1%
ValueCountFrequency (%)
1824133
 
< 0.1%
1824121
 
< 0.1%
1824116
 
< 0.1%
1824101
 
< 0.1%
1824092
 
< 0.1%
1824038
< 0.1%
1824012
 
< 0.1%
1823412
 
< 0.1%
1823381
 
< 0.1%
18233715
0.1%

x
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15430
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184868.252
Minimum81317
Maximum357897
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size181.2 KiB
2022-09-22T18:27:41.364309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum81317
5-th percentile105594.2
Q1157090
median179200
Q3212096.5
95-th percentile279399.9
Maximum357897
Range276580
Interquartile range (IQR)55006.5

Descriptive statistics

Standard deviation49472.36129
Coefficient of variation (CV)0.2676087471
Kurtosis0.03612058982
Mean184868.252
Median Absolute Deviation (MAD)27070
Skewness0.444837951
Sum4284321739
Variance2447514531
MonotonicityNot monotonic
2022-09-22T18:27:41.439378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17180027
 
0.1%
21025627
 
0.1%
19121227
 
0.1%
16249125
 
0.1%
18729924
 
0.1%
16430623
 
0.1%
18711623
 
0.1%
16179223
 
0.1%
20939123
 
0.1%
16623122
 
0.1%
Other values (15420)22931
98.9%
ValueCountFrequency (%)
813171
< 0.1%
824001
< 0.1%
830491
< 0.1%
833121
< 0.1%
834012
< 0.1%
836211
< 0.1%
837911
< 0.1%
841191
< 0.1%
842351
< 0.1%
846711
< 0.1%
ValueCountFrequency (%)
3578971
< 0.1%
3577971
< 0.1%
3571421
< 0.1%
3550971
< 0.1%
3548971
< 0.1%
3513971
< 0.1%
3488971
< 0.1%
3488941
< 0.1%
3486951
< 0.1%
3485971
< 0.1%

y
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15821
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean385179.1603
Minimum4543
Maximum608781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size181.2 KiB
2022-09-22T18:27:41.517576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4543
5-th percentile146347.5
Q1269054
median445575
Q3487159
95-th percentile537448
Maximum608781
Range604238
Interquartile range (IQR)218105

Descriptive statistics

Standard deviation135704.1142
Coefficient of variation (CV)0.3523142687
Kurtosis-0.3417675405
Mean385179.1603
Median Absolute Deviation (MAD)71177
Skewness-0.830387953
Sum8926527040
Variance1.84156066 × 1010
MonotonicityNot monotonic
2022-09-22T18:27:41.593462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50498927
 
0.1%
40099325
 
0.1%
44870525
 
0.1%
50471624
 
0.1%
51903123
 
0.1%
45500023
 
0.1%
50998623
 
0.1%
44310523
 
0.1%
46230221
 
0.1%
52625821
 
0.1%
Other values (15811)22940
99.0%
ValueCountFrequency (%)
45431
< 0.1%
55961
< 0.1%
56321
< 0.1%
56531
< 0.1%
58951
< 0.1%
62991
< 0.1%
67711
< 0.1%
67771
< 0.1%
68961
< 0.1%
69601
< 0.1%
ValueCountFrequency (%)
6087811
< 0.1%
5758852
< 0.1%
5739061
< 0.1%
5732041
< 0.1%
5731741
< 0.1%
5727081
< 0.1%
5715991
< 0.1%
5713261
< 0.1%
5707021
< 0.1%
5702872
< 0.1%

lat
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.7 MiB

lon
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.6 MiB

DataAlerta
Categorical

HIGH CARDINALITY

Distinct346
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2015-08-09 00:00:00.000
 
406
2015-08-10 00:00:00.000
 
326
2015-08-08 00:00:00.000
 
318
2015-04-04 00:00:00.000
 
263
2015-08-07 00:00:00.000
 
233
Other values (341)
21629 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters533025
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st row2015-03-24 00:00:00.000
2nd row2015-03-24 00:00:00.000
3rd row2015-03-24 00:00:00.000
4th row2015-03-24 00:00:00.000
5th row2015-03-24 00:00:00.000

Common Values

ValueCountFrequency (%)
2015-08-09 00:00:00.000406
 
1.8%
2015-08-10 00:00:00.000326
 
1.4%
2015-08-08 00:00:00.000318
 
1.4%
2015-04-04 00:00:00.000263
 
1.1%
2015-08-07 00:00:00.000233
 
1.0%
2015-08-11 00:00:00.000233
 
1.0%
2015-04-05 00:00:00.000220
 
0.9%
2015-08-21 00:00:00.000211
 
0.9%
2015-07-09 00:00:00.000203
 
0.9%
2015-08-05 00:00:00.000203
 
0.9%
Other values (336)20559
88.7%

Length

2022-09-22T18:27:41.810911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00.00023175
50.0%
2015-08-09406
 
0.9%
2015-08-10326
 
0.7%
2015-08-08318
 
0.7%
2015-04-04263
 
0.6%
2015-08-07233
 
0.5%
2015-08-11233
 
0.5%
2015-04-05220
 
0.5%
2015-08-21211
 
0.5%
2015-08-20203
 
0.4%
Other values (337)20762
44.8%

Most occurring characters

ValueCountFrequency (%)
0264898
49.7%
-46350
 
8.7%
:46350
 
8.7%
134821
 
6.5%
233279
 
6.2%
527338
 
5.1%
23175
 
4.3%
.23175
 
4.3%
86990
 
1.3%
76780
 
1.3%
Other values (4)19869
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number393975
73.9%
Other Punctuation69525
 
13.0%
Dash Punctuation46350
 
8.7%
Space Separator23175
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0264898
67.2%
134821
 
8.8%
233279
 
8.4%
527338
 
6.9%
86990
 
1.8%
76780
 
1.7%
35910
 
1.5%
95305
 
1.3%
64663
 
1.2%
43991
 
1.0%
Other Punctuation
ValueCountFrequency (%)
:46350
66.7%
.23175
33.3%
Dash Punctuation
ValueCountFrequency (%)
-46350
100.0%
Space Separator
ValueCountFrequency (%)
23175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common533025
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0264898
49.7%
-46350
 
8.7%
:46350
 
8.7%
134821
 
6.5%
233279
 
6.2%
527338
 
5.1%
23175
 
4.3%
.23175
 
4.3%
86990
 
1.3%
76780
 
1.3%
Other values (4)19869
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII533025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0264898
49.7%
-46350
 
8.7%
:46350
 
8.7%
134821
 
6.5%
233279
 
6.2%
527338
 
5.1%
23175
 
4.3%
.23175
 
4.3%
86990
 
1.3%
76780
 
1.3%
Other values (4)19869
 
3.7%

HoraAlerta
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size1.1 MiB

DataExtincao
Categorical

HIGH CARDINALITY
MISSING

Distinct349
Distinct (%)1.5%
Missing312
Missing (%)1.3%
Memory size1.8 MiB
2015-08-09 00:00:00.000
 
417
2015-08-10 00:00:00.000
 
321
2015-08-08 00:00:00.000
 
286
2015-04-04 00:00:00.000
 
250
2015-08-11 00:00:00.000
 
243
Other values (344)
21346 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters525849
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st row2015-03-24 00:00:00.000
2nd row2015-03-24 00:00:00.000
3rd row2015-03-24 00:00:00.000
4th row2015-03-24 00:00:00.000
5th row2015-03-24 00:00:00.000

Common Values

ValueCountFrequency (%)
2015-08-09 00:00:00.000417
 
1.8%
2015-08-10 00:00:00.000321
 
1.4%
2015-08-08 00:00:00.000286
 
1.2%
2015-04-04 00:00:00.000250
 
1.1%
2015-08-11 00:00:00.000243
 
1.0%
2015-08-07 00:00:00.000234
 
1.0%
2015-04-05 00:00:00.000233
 
1.0%
2015-07-09 00:00:00.000209
 
0.9%
2015-08-21 00:00:00.000202
 
0.9%
2015-08-05 00:00:00.000199
 
0.9%
Other values (339)20269
87.5%
(Missing)312
 
1.3%

Length

2022-09-22T18:27:41.865593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00.00022863
50.0%
2015-08-09417
 
0.9%
2015-08-10321
 
0.7%
2015-08-08286
 
0.6%
2015-04-04250
 
0.5%
2015-08-11243
 
0.5%
2015-08-07234
 
0.5%
2015-04-05233
 
0.5%
2015-07-09209
 
0.5%
2015-08-21202
 
0.4%
Other values (340)20468
44.8%

Most occurring characters

ValueCountFrequency (%)
0261303
49.7%
-45726
 
8.7%
:45726
 
8.7%
134403
 
6.5%
232799
 
6.2%
526939
 
5.1%
22863
 
4.3%
.22863
 
4.3%
86848
 
1.3%
76709
 
1.3%
Other values (4)19670
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number388671
73.9%
Other Punctuation68589
 
13.0%
Dash Punctuation45726
 
8.7%
Space Separator22863
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0261303
67.2%
134403
 
8.9%
232799
 
8.4%
526939
 
6.9%
86848
 
1.8%
76709
 
1.7%
35828
 
1.5%
95260
 
1.4%
64607
 
1.2%
43975
 
1.0%
Other Punctuation
ValueCountFrequency (%)
:45726
66.7%
.22863
33.3%
Dash Punctuation
ValueCountFrequency (%)
-45726
100.0%
Space Separator
ValueCountFrequency (%)
22863
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common525849
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0261303
49.7%
-45726
 
8.7%
:45726
 
8.7%
134403
 
6.5%
232799
 
6.2%
526939
 
5.1%
22863
 
4.3%
.22863
 
4.3%
86848
 
1.3%
76709
 
1.3%
Other values (4)19670
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII525849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0261303
49.7%
-45726
 
8.7%
:45726
 
8.7%
134403
 
6.5%
232799
 
6.2%
526939
 
5.1%
22863
 
4.3%
.22863
 
4.3%
86848
 
1.3%
76709
 
1.3%
Other values (4)19670
 
3.7%

HoraExtincao
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing317
Missing (%)1.4%
Memory size1.1 MiB

Data1Intervencao
Categorical

HIGH CARDINALITY
MISSING

Distinct346
Distinct (%)1.6%
Missing1199
Missing (%)5.2%
Memory size1.7 MiB
2015-08-09 00:00:00.000
 
394
2015-08-10 00:00:00.000
 
312
2015-08-08 00:00:00.000
 
297
2015-04-04 00:00:00.000
 
248
2015-08-11 00:00:00.000
 
223
Other values (341)
20502 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters505448
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.1%

Sample

1st row2015-03-24 00:00:00.000
2nd row2015-03-24 00:00:00.000
3rd row2015-03-24 00:00:00.000
4th row2015-03-24 00:00:00.000
5th row2015-03-24 00:00:00.000

Common Values

ValueCountFrequency (%)
2015-08-09 00:00:00.000394
 
1.7%
2015-08-10 00:00:00.000312
 
1.3%
2015-08-08 00:00:00.000297
 
1.3%
2015-04-04 00:00:00.000248
 
1.1%
2015-08-11 00:00:00.000223
 
1.0%
2015-08-07 00:00:00.000222
 
1.0%
2015-04-05 00:00:00.000218
 
0.9%
2015-08-21 00:00:00.000203
 
0.9%
2015-08-05 00:00:00.000194
 
0.8%
2015-08-20 00:00:00.000193
 
0.8%
Other values (336)19472
84.0%
(Missing)1199
 
5.2%

Length

2022-09-22T18:27:41.919230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00.00021976
50.0%
2015-08-09394
 
0.9%
2015-08-10312
 
0.7%
2015-08-08297
 
0.7%
2015-04-04248
 
0.6%
2015-08-11223
 
0.5%
2015-08-07222
 
0.5%
2015-04-05218
 
0.5%
2015-08-21203
 
0.5%
2015-08-05194
 
0.4%
Other values (337)19665
44.7%

Most occurring characters

ValueCountFrequency (%)
0251241
49.7%
-43952
 
8.7%
:43952
 
8.7%
132992
 
6.5%
231520
 
6.2%
525942
 
5.1%
21976
 
4.3%
.21976
 
4.3%
86598
 
1.3%
76410
 
1.3%
Other values (4)18889
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number373592
73.9%
Other Punctuation65928
 
13.0%
Dash Punctuation43952
 
8.7%
Space Separator21976
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0251241
67.3%
132992
 
8.8%
231520
 
8.4%
525942
 
6.9%
86598
 
1.8%
76410
 
1.7%
35634
 
1.5%
95022
 
1.3%
64415
 
1.2%
43818
 
1.0%
Other Punctuation
ValueCountFrequency (%)
:43952
66.7%
.21976
33.3%
Dash Punctuation
ValueCountFrequency (%)
-43952
100.0%
Space Separator
ValueCountFrequency (%)
21976
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common505448
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0251241
49.7%
-43952
 
8.7%
:43952
 
8.7%
132992
 
6.5%
231520
 
6.2%
525942
 
5.1%
21976
 
4.3%
.21976
 
4.3%
86598
 
1.3%
76410
 
1.3%
Other values (4)18889
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII505448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0251241
49.7%
-43952
 
8.7%
:43952
 
8.7%
132992
 
6.5%
231520
 
6.2%
525942
 
5.1%
21976
 
4.3%
.21976
 
4.3%
86598
 
1.3%
76410
 
1.3%
Other values (4)18889
 
3.7%

Hora1Intervencao
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1213
Missing (%)5.2%
Memory size1.0 MiB

FonteAlerta
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing23175
Missing (%)100.0%
Memory size181.2 KiB

NUT
Categorical

HIGH CARDINALITY

Distinct3861
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
PT11040410
 
86
PT11051013
 
86
PT11050406
 
69
PT11040409
 
69
PT13030603
 
62
Other values (3856)
22803 

Length

Max length10
Median length10
Mean length9.997152104
Min length7

Characters and Unicode

Total characters231684
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique887 ?
Unique (%)3.8%

Sample

1st rowPT11010746
2nd rowPT11050731
3rd rowPT11021323
4th rowPT11010709
5th rowPT12050304

Common Values

ValueCountFrequency (%)
PT1104041086
 
0.4%
PT1105101386
 
0.4%
PT1105040669
 
0.3%
PT1104040969
 
0.3%
PT1303060362
 
0.3%
PT1104171557
 
0.2%
PT1105050552
 
0.2%
PT1303070251
 
0.2%
PT1105013550
 
0.2%
PT1108060150
 
0.2%
Other values (3851)22543
97.3%

Length

2022-09-22T18:27:41.975870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pt1104041086
 
0.4%
pt1105101386
 
0.4%
pt1105040669
 
0.3%
pt1104040969
 
0.3%
pt1303060362
 
0.3%
pt1104171557
 
0.2%
pt1105050552
 
0.2%
pt1303070251
 
0.2%
pt1108060150
 
0.2%
pt1105090550
 
0.2%
Other values (3851)22543
97.3%

Most occurring characters

ValueCountFrequency (%)
160751
26.2%
053190
23.0%
P23175
 
10.0%
T23175
 
10.0%
315584
 
6.7%
215076
 
6.5%
510621
 
4.6%
410122
 
4.4%
85432
 
2.3%
75264
 
2.3%
Other values (2)9294
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number185334
80.0%
Uppercase Letter46350
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
160751
32.8%
053190
28.7%
315584
 
8.4%
215076
 
8.1%
510621
 
5.7%
410122
 
5.5%
85432
 
2.9%
75264
 
2.8%
65184
 
2.8%
94110
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
P23175
50.0%
T23175
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common185334
80.0%
Latin46350
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
160751
32.8%
053190
28.7%
315584
 
8.4%
215076
 
8.1%
510621
 
5.7%
410122
 
5.5%
85432
 
2.9%
75264
 
2.8%
65184
 
2.8%
94110
 
2.2%
Latin
ValueCountFrequency (%)
P23175
50.0%
T23175
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII231684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
160751
26.2%
053190
23.0%
P23175
 
10.0%
T23175
 
10.0%
315584
 
6.7%
215076
 
6.5%
510621
 
4.6%
410122
 
4.4%
85432
 
2.3%
75264
 
2.3%
Other values (2)9294
 
4.0%

AA_Povoamento (ha)
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1023
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.02467149
Minimum0
Maximum1548.92
Zeros18165
Zeros (%)78.4%
Negative0
Negative (%)0.0%
Memory size181.2 KiB
2022-09-22T18:27:42.042388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.1515
Maximum1548.92
Range1548.92
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.48799852
Coefficient of variation (CV)19.01877697
Kurtosis2771.120365
Mean1.02467149
Median Absolute Deviation (MAD)0
Skewness46.40516403
Sum23746.76177
Variance379.7820864
MonotonicityNot monotonic
2022-09-22T18:27:42.117557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018165
78.4%
0.01311
 
1.3%
0.1273
 
1.2%
0.5264
 
1.1%
1247
 
1.1%
0.05241
 
1.0%
0.02215
 
0.9%
0.2212
 
0.9%
0.03151
 
0.7%
2126
 
0.5%
Other values (1013)2970
 
12.8%
ValueCountFrequency (%)
018165
78.4%
0.000110
 
< 0.1%
0.00025
 
< 0.1%
0.00033
 
< 0.1%
0.00042
 
< 0.1%
0.00057
 
< 0.1%
0.00061
 
< 0.1%
0.0006361
 
< 0.1%
0.00092
 
< 0.1%
0.00154
 
0.2%
ValueCountFrequency (%)
1548.921
< 0.1%
10821
< 0.1%
10481
< 0.1%
6861
< 0.1%
593.831
< 0.1%
583.45761
< 0.1%
576.151
< 0.1%
5501
< 0.1%
5301
< 0.1%
5011
< 0.1%

AA_Mato (ha)
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1886
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.75608107
Minimum0
Maximum4499
Zeros9884
Zeros (%)42.6%
Negative0
Negative (%)0.0%
Memory size181.2 KiB
2022-09-22T18:27:42.196861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.005
Q30.1
95-th percentile2.403
Maximum4499
Range4499
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation40.18774792
Coefficient of variation (CV)22.8849047
Kurtosis7842.249642
Mean1.75608107
Median Absolute Deviation (MAD)0.005
Skewness80.19057206
Sum40697.17879
Variance1615.055083
MonotonicityNot monotonic
2022-09-22T18:27:42.267885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09884
42.6%
0.011245
 
5.4%
0.02792
 
3.4%
0.05735
 
3.2%
0.1651
 
2.8%
0.005570
 
2.5%
0.5516
 
2.2%
0.001497
 
2.1%
0.03433
 
1.9%
1429
 
1.9%
Other values (1876)7423
32.0%
ValueCountFrequency (%)
09884
42.6%
1 × 10-51
 
< 0.1%
2 × 10-52
 
< 0.1%
5 × 10-52
 
< 0.1%
7 × 10-51
 
< 0.1%
0.0001132
 
0.6%
0.000274
 
0.3%
0.000328
 
0.1%
0.000414
 
0.1%
0.000451
 
< 0.1%
ValueCountFrequency (%)
44991
< 0.1%
2543.561
< 0.1%
2129.51
< 0.1%
10311
< 0.1%
955.111
< 0.1%
734.561
< 0.1%
678.821
< 0.1%
5551
< 0.1%
520.141
< 0.1%
4581
< 0.1%

AA_Agricola (ha)
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1160
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1677520227
Minimum0
Maximum224.16
Zeros19012
Zeros (%)82.0%
Negative0
Negative (%)0.0%
Memory size181.2 KiB
2022-09-22T18:27:42.341477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.15
Maximum224.16
Range224.16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.089876847
Coefficient of variation (CV)18.41931201
Kurtosis2299.7018
Mean0.1677520227
Median Absolute Deviation (MAD)0
Skewness42.94837127
Sum3887.653127
Variance9.547338927
MonotonicityNot monotonic
2022-09-22T18:27:42.412750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019012
82.0%
0.01288
 
1.2%
0.001239
 
1.0%
0.005232
 
1.0%
0.02201
 
0.9%
0.05145
 
0.6%
0.002145
 
0.6%
0.03136
 
0.6%
0.0001105
 
0.5%
0.186
 
0.4%
Other values (1150)2586
 
11.2%
ValueCountFrequency (%)
019012
82.0%
5 × 10-52
 
< 0.1%
0.0001105
 
0.5%
0.000152
 
< 0.1%
0.000244
 
0.2%
0.000327
 
0.1%
0.0003541
 
< 0.1%
0.000422
 
0.1%
0.000431
 
< 0.1%
0.000566
 
0.3%
ValueCountFrequency (%)
224.161
< 0.1%
1651
< 0.1%
1581
< 0.1%
127.51
< 0.1%
1201
< 0.1%
119.651
< 0.1%
1091
< 0.1%
841
< 0.1%
731
< 0.1%
671
< 0.1%

AA_EspacosFlorestais (pov+mato)(ha)
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct2477
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.780752559
Minimum0
Maximum4661
Zeros6824
Zeros (%)29.4%
Negative0
Negative (%)0.0%
Memory size181.2 KiB
2022-09-22T18:27:42.483936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.23
95-th percentile4
Maximum4661
Range4661
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation49.35001617
Coefficient of variation (CV)17.74700018
Kurtosis4456.337352
Mean2.780752559
Median Absolute Deviation (MAD)0.02
Skewness58.04817374
Sum64443.94056
Variance2435.424096
MonotonicityNot monotonic
2022-09-22T18:27:42.556856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06824
29.4%
0.011437
 
6.2%
0.02924
 
4.0%
0.05867
 
3.7%
0.1778
 
3.4%
0.005640
 
2.8%
0.5561
 
2.4%
0.001529
 
2.3%
0.03517
 
2.2%
1481
 
2.1%
Other values (2467)9617
41.5%
ValueCountFrequency (%)
06824
29.4%
1 × 10-51
 
< 0.1%
2 × 10-52
 
< 0.1%
5 × 10-52
 
< 0.1%
7 × 10-51
 
< 0.1%
0.0001142
 
0.6%
0.000279
 
0.3%
0.000331
 
0.1%
0.000416
 
0.1%
0.000451
 
< 0.1%
ValueCountFrequency (%)
46611
< 0.1%
3023.991
< 0.1%
2501.51
< 0.1%
1574.031
< 0.1%
14221
< 0.1%
11831
< 0.1%
11581
< 0.1%
11051
< 0.1%
9851
< 0.1%
8451
< 0.1%

AA_Total (pov+mato+agric) (ha)
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct3032
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.948504582
Minimum0
Maximum4673
Zeros2925
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size181.2 KiB
2022-09-22T18:27:42.628073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.005
median0.04
Q30.34
95-th percentile4.26
Maximum4673
Range4673
Interquartile range (IQR)0.335

Descriptive statistics

Standard deviation50.08396981
Coefficient of variation (CV)16.98622757
Kurtosis4276.294514
Mean2.948504582
Median Absolute Deviation (MAD)0.04
Skewness56.72923777
Sum68331.59368
Variance2508.404032
MonotonicityNot monotonic
2022-09-22T18:27:42.700743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02925
 
12.6%
0.011708
 
7.4%
0.021112
 
4.8%
0.051003
 
4.3%
0.005871
 
3.8%
0.1855
 
3.7%
0.001765
 
3.3%
0.03648
 
2.8%
0.5630
 
2.7%
1527
 
2.3%
Other values (3022)12131
52.3%
ValueCountFrequency (%)
02925
12.6%
1 × 10-51
 
< 0.1%
2 × 10-52
 
< 0.1%
5 × 10-53
 
< 0.1%
7 × 10-51
 
< 0.1%
0.0001247
 
1.1%
0.000152
 
< 0.1%
0.0002123
 
0.5%
0.000358
 
0.3%
0.0003541
 
< 0.1%
ValueCountFrequency (%)
46731
< 0.1%
3023.991
< 0.1%
2557.31
< 0.1%
15801
< 0.1%
1574.031
< 0.1%
11901
< 0.1%
11831
< 0.1%
11051
< 0.1%
9851
< 0.1%
8451
< 0.1%

Reacendimentos
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
21742 
1
 
1433

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23175
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
021742
93.8%
11433
 
6.2%

Length

2022-09-22T18:27:42.765909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T18:27:42.817796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
021742
93.8%
11433
 
6.2%

Most occurring characters

ValueCountFrequency (%)
021742
93.8%
11433
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number23175
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
021742
93.8%
11433
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common23175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
021742
93.8%
11433
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII23175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
021742
93.8%
11433
 
6.2%

Queimada
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
22552 
1
 
623

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23175
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
022552
97.3%
1623
 
2.7%

Length

2022-09-22T18:27:42.863926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T18:27:42.916206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
022552
97.3%
1623
 
2.7%

Most occurring characters

ValueCountFrequency (%)
022552
97.3%
1623
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number23175
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
022552
97.3%
1623
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common23175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
022552
97.3%
1623
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII23175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
022552
97.3%
1623
 
2.7%

Falso Alarme
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
20250 
1
2925 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23175
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
020250
87.4%
12925
 
12.6%

Length

2022-09-22T18:27:42.962206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T18:27:43.014396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
020250
87.4%
12925
 
12.6%

Most occurring characters

ValueCountFrequency (%)
020250
87.4%
12925
 
12.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number23175
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
020250
87.4%
12925
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
Common23175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
020250
87.4%
12925
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII23175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
020250
87.4%
12925
 
12.6%

Fogacho
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
12527 
0
10648 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23175
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
112527
54.1%
010648
45.9%

Length

2022-09-22T18:27:43.062090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T18:27:43.115042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
112527
54.1%
010648
45.9%

Most occurring characters

ValueCountFrequency (%)
112527
54.1%
010648
45.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number23175
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
112527
54.1%
010648
45.9%

Most occurring scripts

ValueCountFrequency (%)
Common23175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
112527
54.1%
010648
45.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII23175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112527
54.1%
010648
45.9%

Incendio
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
19851 
1
3324 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23175
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019851
85.7%
13324
 
14.3%

Length

2022-09-22T18:27:43.163268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T18:27:43.215912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
019851
85.7%
13324
 
14.3%

Most occurring characters

ValueCountFrequency (%)
019851
85.7%
13324
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number23175
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
019851
85.7%
13324
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common23175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
019851
85.7%
13324
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII23175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
019851
85.7%
13324
 
14.3%

Agricola
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
19399 
1
3776 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23175
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
019399
83.7%
13776
 
16.3%

Length

2022-09-22T18:27:43.263875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T18:27:43.316268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
019399
83.7%
13776
 
16.3%

Most occurring characters

ValueCountFrequency (%)
019399
83.7%
13776
 
16.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number23175
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
019399
83.7%
13776
 
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common23175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
019399
83.7%
13776
 
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII23175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
019399
83.7%
13776
 
16.3%

Perimetro
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct87
Distinct (%)10.5%
Missing22349
Missing (%)96.4%
Memory size758.9 KiB
Barroso
121 
Serra da Cabreira
63 
Soajo e Peneda
 
45
Alvão
 
36
Serra amarela
 
34
Other values (82)
527 

Length

Max length35
Median length24
Mean length13.7094431
Min length4

Characters and Unicode

Total characters11324
Distinct characters48
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)2.9%

Sample

1st rowPampilhosa da Serra
2nd rowMondim de Basto
3rd rowSoajo e Peneda
4th rowRibeira de Pena
5th rowSerra do Leomil

Common Values

ValueCountFrequency (%)
Barroso121
 
0.5%
Serra da Cabreira63
 
0.3%
Soajo e Peneda45
 
0.2%
Alvão36
 
0.2%
Serra amarela34
 
0.1%
Ribeira de Pena31
 
0.1%
Chaves28
 
0.1%
Boalhosa26
 
0.1%
Serra de Anta23
 
0.1%
Serra da Padrela22
 
0.1%
Other values (77)397
 
1.7%
(Missing)22349
96.4%

Length

2022-09-22T18:27:43.375132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
serra294
 
13.8%
de172
 
8.1%
da169
 
7.9%
barroso121
 
5.7%
e117
 
5.5%
do65
 
3.1%
cabreira63
 
3.0%
são53
 
2.5%
soajo45
 
2.1%
peneda45
 
2.1%
Other values (120)986
46.3%

Most occurring characters

ValueCountFrequency (%)
a1739
15.4%
r1384
12.2%
1304
11.5%
e1266
11.2%
o941
 
8.3%
d600
 
5.3%
S457
 
4.0%
i409
 
3.6%
s358
 
3.2%
n323
 
2.9%
Other values (38)2543
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8482
74.9%
Uppercase Letter1538
 
13.6%
Space Separator1304
 
11.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1739
20.5%
r1384
16.3%
e1266
14.9%
o941
11.1%
d600
 
7.1%
i409
 
4.8%
s358
 
4.2%
n323
 
3.8%
l314
 
3.7%
t270
 
3.2%
Other values (18)878
10.4%
Uppercase Letter
ValueCountFrequency (%)
S457
29.7%
M169
 
11.0%
B167
 
10.9%
C164
 
10.7%
P137
 
8.9%
A98
 
6.4%
R52
 
3.4%
V50
 
3.3%
L49
 
3.2%
E45
 
2.9%
Other values (9)150
 
9.8%
Space Separator
ValueCountFrequency (%)
1304
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10020
88.5%
Common1304
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1739
17.4%
r1384
13.8%
e1266
12.6%
o941
9.4%
d600
 
6.0%
S457
 
4.6%
i409
 
4.1%
s358
 
3.6%
n323
 
3.2%
l314
 
3.1%
Other values (37)2229
22.2%
Common
ValueCountFrequency (%)
1304
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11160
98.6%
None164
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1739
15.6%
r1384
12.4%
1304
11.7%
e1266
11.3%
o941
 
8.4%
d600
 
5.4%
S457
 
4.1%
i409
 
3.7%
s358
 
3.2%
n323
 
2.9%
Other values (32)2379
21.3%
None
ValueCountFrequency (%)
ã126
76.8%
Á19
 
11.6%
ç8
 
4.9%
ó5
 
3.0%
é5
 
3.0%
ô1
 
0.6%

APS
Categorical

HIGH CORRELATION
MISSING

Distinct28
Distinct (%)2.6%
Missing22116
Missing (%)95.4%
Memory size775.2 KiB
PENEDA-GERÊS
200 
SERRA DA ESTRELA
193 
DOURO INTERNACIONAL
141 
MONTESINHO
121 
SINTRA-CASCAIS
85 
Other values (23)
319 

Length

Max length50
Median length37
Mean length16.84796978
Min length5

Characters and Unicode

Total characters17842
Distinct characters42
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.8%

Sample

1st rowSERRA DE SÃO MAMEDE
2nd rowSERRA DE SÃO MAMEDE
3rd rowSERRA DA ESTRELA
4th rowARRÁBIDA
5th rowDOURO INTERNACIONAL

Common Values

ValueCountFrequency (%)
PENEDA-GERÊS200
 
0.9%
SERRA DA ESTRELA193
 
0.8%
DOURO INTERNACIONAL141
 
0.6%
MONTESINHO121
 
0.5%
SINTRA-CASCAIS85
 
0.4%
SERRAS DE AIRE E CANDEEIROS84
 
0.4%
SUDOESTE ALENTEJANO E COSTA VICENTINA55
 
0.2%
ARRÁBIDA40
 
0.2%
SERRA DE SÃO MAMEDE26
 
0.1%
SERRA DE MONTEJUNTO21
 
0.1%
Other values (18)93
 
0.4%
(Missing)22116
95.4%

Length

2022-09-22T18:27:43.445771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
serra243
 
9.7%
da224
 
9.0%
peneda-gerês200
 
8.0%
estrela193
 
7.7%
de149
 
6.0%
e143
 
5.7%
internacional142
 
5.7%
douro141
 
5.6%
montesinho121
 
4.8%
sintra-cascais85
 
3.4%
Other values (51)857
34.3%

Most occurring characters

ValueCountFrequency (%)
E2508
14.1%
A2245
12.6%
R1771
9.9%
S1567
8.8%
1439
8.1%
N1351
7.6%
O1145
 
6.4%
I1010
 
5.7%
D996
 
5.6%
T849
 
4.8%
Other values (32)2961
16.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter16095
90.2%
Space Separator1439
 
8.1%
Dash Punctuation285
 
1.6%
Lowercase Letter21
 
0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E2508
15.6%
A2245
13.9%
R1771
11.0%
S1567
9.7%
N1351
8.4%
O1145
7.1%
I1010
6.3%
D996
 
6.2%
T849
 
5.3%
C577
 
3.6%
Other values (19)2076
12.9%
Lowercase Letter
ValueCountFrequency (%)
r6
28.6%
e4
19.0%
t2
 
9.5%
á2
 
9.5%
i2
 
9.5%
a2
 
9.5%
s1
 
4.8%
b1
 
4.8%
d1
 
4.8%
Space Separator
ValueCountFrequency (%)
1439
100.0%
Dash Punctuation
ValueCountFrequency (%)
-285
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16116
90.3%
Common1726
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E2508
15.6%
A2245
13.9%
R1771
11.0%
S1567
9.7%
N1351
8.4%
O1145
7.1%
I1010
6.3%
D996
 
6.2%
T849
 
5.3%
C577
 
3.6%
Other values (28)2097
13.0%
Common
ValueCountFrequency (%)
1439
83.4%
-285
 
16.5%
(1
 
0.1%
)1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII17519
98.2%
None323
 
1.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E2508
14.3%
A2245
12.8%
R1771
10.1%
S1567
8.9%
1439
8.2%
N1351
7.7%
O1145
6.5%
I1010
 
5.8%
D996
 
5.7%
T849
 
4.8%
Other values (24)2638
15.1%
None
ValueCountFrequency (%)
Ê200
61.9%
Á55
 
17.0%
Ã40
 
12.4%
Ó17
 
5.3%
È7
 
2.2%
á2
 
0.6%
É1
 
0.3%
Ú1
 
0.3%

Causa
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct92
Distinct (%)0.6%
Missing7013
Missing (%)30.3%
Infinite0
Infinite (%)0.0%
Mean356.4273605
Minimum2
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size181.2 KiB
2022-09-22T18:27:43.514687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile14
Q1124
median448
Q3630
95-th percentile711
Maximum711
Range709
Interquartile range (IQR)506

Descriptive statistics

Standard deviation246.9737621
Coefficient of variation (CV)0.6929147128
Kurtosis-1.652877711
Mean356.4273605
Median Absolute Deviation (MAD)263
Skewness0.07789879425
Sum5760579
Variance60996.03918
MonotonicityNot monotonic
2022-09-22T18:27:43.590168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6302517
 
10.9%
4482051
 
8.9%
7111536
 
6.6%
6101504
 
6.5%
1251012
 
4.4%
121884
 
3.8%
60794
 
3.4%
124792
 
3.4%
122692
 
3.0%
449577
 
2.5%
Other values (82)3803
16.4%
(Missing)7013
30.3%
ValueCountFrequency (%)
22
 
< 0.1%
34
 
< 0.1%
4176
 
0.8%
52
 
< 0.1%
6561
2.4%
1123
 
0.1%
1222
 
0.1%
1311
 
< 0.1%
1416
 
0.1%
1517
 
0.1%
ValueCountFrequency (%)
7111536
6.6%
6302517
10.9%
62057
 
0.2%
6101504
6.5%
449577
 
2.5%
4482051
8.9%
44628
 
0.1%
44518
 
0.1%
44418
 
0.1%
4415
 
< 0.1%

TipoCausa
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing7013
Missing (%)30.3%
Memory size1.3 MiB
Negligente
5957 
Desconhecida
5433 
Intencional
3086 
Reacendimento
1536 
Natural
 
150

Length

Max length13
Median length12
Mean length11.12052964
Min length7

Characters and Unicode

Total characters179730
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNegligente
2nd rowNegligente
3rd rowDesconhecida
4th rowDesconhecida
5th rowDesconhecida

Common Values

ValueCountFrequency (%)
Negligente5957
25.7%
Desconhecida5433
23.4%
Intencional3086
13.3%
Reacendimento1536
 
6.6%
Natural150
 
0.6%
(Missing)7013
30.3%

Length

2022-09-22T18:27:43.664805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T18:27:43.734344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
negligente5957
36.9%
desconhecida5433
33.6%
intencional3086
19.1%
reacendimento1536
 
9.5%
natural150
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e36431
20.3%
n23720
13.2%
i16012
8.9%
c15488
8.6%
g11914
 
6.6%
t10729
 
6.0%
a10355
 
5.8%
o10055
 
5.6%
l9193
 
5.1%
d6969
 
3.9%
Other values (9)28864
16.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter163568
91.0%
Uppercase Letter16162
 
9.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e36431
22.3%
n23720
14.5%
i16012
9.8%
c15488
9.5%
g11914
 
7.3%
t10729
 
6.6%
a10355
 
6.3%
o10055
 
6.1%
l9193
 
5.6%
d6969
 
4.3%
Other values (5)12702
 
7.8%
Uppercase Letter
ValueCountFrequency (%)
N6107
37.8%
D5433
33.6%
I3086
19.1%
R1536
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
Latin179730
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e36431
20.3%
n23720
13.2%
i16012
8.9%
c15488
8.6%
g11914
 
6.6%
t10729
 
6.0%
a10355
 
5.8%
o10055
 
5.6%
l9193
 
5.1%
d6969
 
3.9%
Other values (9)28864
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII179730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e36431
20.3%
n23720
13.2%
i16012
8.9%
c15488
8.6%
g11914
 
6.6%
t10729
 
6.0%
a10355
 
5.8%
o10055
 
5.6%
l9193
 
5.1%
d6969
 
3.9%
Other values (9)28864
16.1%

Região PROF
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size1.6 MiB
Entre Douro e Minho
9024 
Ribatejo e Oeste
4694 
Beira Litoral
2767 
Trás-os-Montes
2195 
-
1279 
Other values (6)
3214 

Length

Max length21
Median length19
Mean length14.98908212
Min length1

Characters and Unicode

Total characters347342
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntre Douro e Minho
2nd rowEntre Douro e Minho
3rd rowEntre Douro e Minho
4th rowEntre Douro e Minho
5th rowBeira Litoral

Common Values

ValueCountFrequency (%)
Entre Douro e Minho9024
38.9%
Ribatejo e Oeste4694
20.3%
Beira Litoral2767
 
11.9%
Trás-os-Montes2195
 
9.5%
-1279
 
5.5%
Alentejo1264
 
5.5%
Beira Interior1185
 
5.1%
Algarve583
 
2.5%
Norte78
 
0.3%
Lisboa e Vale do Tejo67
 
0.3%

Length

2022-09-22T18:27:43.804267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e13785
21.6%
entre9024
14.1%
douro9024
14.1%
minho9024
14.1%
ribatejo4694
 
7.4%
oeste4694
 
7.4%
beira3952
 
6.2%
litoral2767
 
4.3%
trás-os-montes2195
 
3.4%
1279
 
2.0%
Other values (9)3415
 
5.3%

Most occurring characters

ValueCountFrequency (%)
e47583
13.7%
o41688
12.0%
40680
11.7%
r30030
 
8.6%
t25938
 
7.5%
n22729
 
6.5%
i21689
 
6.2%
a12130
 
3.5%
s11346
 
3.3%
M11219
 
3.2%
Other values (22)82310
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter250076
72.0%
Uppercase Letter50917
 
14.7%
Space Separator40680
 
11.7%
Dash Punctuation5669
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e47583
19.0%
o41688
16.7%
r30030
12.0%
t25938
10.4%
n22729
9.1%
i21689
8.7%
a12130
 
4.9%
s11346
 
4.5%
h9024
 
3.6%
u9024
 
3.6%
Other values (7)18895
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
M11219
22.0%
E9024
17.7%
D9024
17.7%
O4694
9.2%
R4694
9.2%
B3952
 
7.8%
L2834
 
5.6%
T2262
 
4.4%
A1847
 
3.6%
I1185
 
2.3%
Other values (3)182
 
0.4%
Space Separator
ValueCountFrequency (%)
40680
100.0%
Dash Punctuation
ValueCountFrequency (%)
-5669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin300993
86.7%
Common46349
 
13.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e47583
15.8%
o41688
13.9%
r30030
10.0%
t25938
 
8.6%
n22729
 
7.6%
i21689
 
7.2%
a12130
 
4.0%
s11346
 
3.8%
M11219
 
3.7%
h9024
 
3.0%
Other values (20)67617
22.5%
Common
ValueCountFrequency (%)
40680
87.8%
-5669
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII345147
99.4%
None2195
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e47583
13.8%
o41688
12.1%
40680
11.8%
r30030
 
8.7%
t25938
 
7.5%
n22729
 
6.6%
i21689
 
6.3%
a12130
 
3.5%
s11346
 
3.3%
M11219
 
3.3%
Other values (21)80115
23.2%
None
ValueCountFrequency (%)
á2195
100.0%

UGF
Categorical

HIGH CORRELATION

Distinct27
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size1.6 MiB
Tâmega
3276 
AML
2330 
AMP e Entre Douro e Vouga
2226 
Baixo Minho
2110 
Centro Litoral
1565 
Other values (22)
11667 

Length

Max length58
Median length25
Mean length10.83498749
Min length1

Characters and Unicode

Total characters251090
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlto Minho
2nd rowTâmega
3rd rowBaixo Minho
4th rowAlto Minho
5th rowDão Lafões

Common Values

ValueCountFrequency (%)
Tâmega3276
14.1%
AML2330
10.1%
AMP e Entre Douro e Vouga2226
 
9.6%
Baixo Minho2110
 
9.1%
Centro Litoral1565
 
6.8%
Alto Minho1412
 
6.1%
Ribatejo1344
 
5.8%
-1279
 
5.5%
Oeste1021
 
4.4%
Douro901
 
3.9%
Other values (17)5710
24.6%

Length

2022-09-22T18:27:43.878669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e5622
 
11.8%
minho3550
 
7.5%
tâmega3312
 
7.0%
douro3163
 
6.7%
baixo2492
 
5.2%
aml2330
 
4.9%
entre2262
 
4.8%
vouga2262
 
4.8%
amp2226
 
4.7%
litoral1814
 
3.8%
Other values (24)18465
38.9%

Most occurring characters

ValueCountFrequency (%)
o28843
 
11.5%
e26646
 
10.6%
24216
 
9.6%
r18825
 
7.5%
a17631
 
7.0%
t15210
 
6.1%
i12816
 
5.1%
n11327
 
4.5%
A8224
 
3.3%
M8208
 
3.3%
Other values (32)79144
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter175880
70.0%
Uppercase Letter49607
 
19.8%
Space Separator24324
 
9.7%
Dash Punctuation1279
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o28843
16.4%
e26646
15.2%
r18825
10.7%
a17631
10.0%
t15210
8.6%
i12816
7.3%
n11327
 
6.4%
l7709
 
4.4%
g6157
 
3.5%
u5781
 
3.3%
Other values (13)24935
14.2%
Uppercase Letter
ValueCountFrequency (%)
A8224
16.6%
M8208
16.5%
L4952
10.0%
B4482
9.0%
D3905
7.9%
P3733
7.5%
T3312
6.7%
V2262
 
4.6%
E2262
 
4.6%
C1863
 
3.8%
Other values (6)6404
12.9%
Space Separator
ValueCountFrequency (%)
24216
99.6%
 108
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
-1279
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin225487
89.8%
Common25603
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o28843
12.8%
e26646
 
11.8%
r18825
 
8.3%
a17631
 
7.8%
t15210
 
6.7%
i12816
 
5.7%
n11327
 
5.0%
A8224
 
3.6%
M8208
 
3.6%
l7709
 
3.4%
Other values (29)70048
31.1%
Common
ValueCountFrequency (%)
24216
94.6%
-1279
 
5.0%
 108
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII246084
98.0%
None5006
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o28843
 
11.7%
e26646
 
10.8%
24216
 
9.8%
r18825
 
7.6%
a17631
 
7.2%
t15210
 
6.2%
i12816
 
5.2%
n11327
 
4.6%
A8224
 
3.3%
M8208
 
3.3%
Other values (27)74138
30.1%
None
ValueCountFrequency (%)
â3312
66.2%
ã742
 
14.8%
õ742
 
14.8%
 108
 
2.2%
Á102
 
2.0%

Interactions

2022-09-22T18:27:38.439557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:31.813557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:32.686000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:33.387432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:34.075599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:34.771511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:35.610723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.283934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.952479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:37.644117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:38.512687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:31.903371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:32.760025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:33.459490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:34.148106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:34.962479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:35.685886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.354354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:37.024446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:37.716150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:38.584231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:31.977102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:32.830625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:33.529050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:34.219092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:35.035232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:35.754522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.423307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:37.094509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:37.785813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:38.652777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:32.048248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:32.899464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:33.596203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:34.286843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:35.106838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:35.819639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.487958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:37.161479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:37.852819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:38.723410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:32.121081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:32.970627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:33.666015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:34.358185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:35.180604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:35.886792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.556117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:37.231381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:38.037605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:38.797736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:32.198771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:33.045795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:33.739222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:34.432388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:35.256405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:35.957626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.627329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:37.304185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:38.109295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:38.864312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:32.267717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:33.112795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:33.805437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:34.499405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:35.324892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.021700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.690763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:37.369043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:38.174324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:38.930893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:32.471974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:33.179481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:33.870416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:34.565619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:35.393884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.084098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.753233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:37.433378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:38.238733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:39.002497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:32.542066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:33.247929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:33.937623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:34.633702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:35.466518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.148780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.819300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:37.505077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:38.305522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:39.069174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:32.611174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:33.314584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:34.004409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:34.700114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:35.534589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.211477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:36.882442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:37.572233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-22T18:27:38.369778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-09-22T18:27:43.952239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-22T18:27:44.085421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-22T18:27:44.223907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-22T18:27:44.352258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-22T18:27:44.466296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-22T18:27:39.318425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-22T18:27:39.922813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-22T18:27:40.204071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-22T18:27:40.363474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

AnoCodigo SGIFCodigo_ANPCTipoDistritoConcelhoFreguesiaLocalINExylatlonDataAlertaHoraAlertaDataExtincaoHoraExtincaoData1IntervencaoHora1IntervencaoFonteAlertaNUTAA_Povoamento (ha)AA_Mato (ha)AA_Agricola (ha)AA_EspacosFlorestais (pov+mato)(ha)AA_Total (pov+mato+agric) (ha)ReacendimentosQueimadaFalso AlarmeFogachoIncendioAgricolaPerimetroAPSCausaTipoCausaRegião PROFUGF
02015DM3152522015160007544FlorestalViana do CasteloPonte de LimaSerdedeloVALE DE TROVELA (SERDEDELO)16074616781453089641:44:48.5663999999878''8:31:12.3276000000027''2015-03-24 00:00:00.00017:01:002015-03-24 00:00:00.00018:09:002015-03-24 00:00:00.00017:10:00NaNPT110107462.500.000.02.502.50000010NaNNaN122.0NegligenteEntre Douro e MinhoAlto Minho
12015DM2153052015130043758FlorestalPortoMarco de CanavesesVila Boa de QuiresLUGAR DO FOFO13073119373147189641:12:58.4280000000109''8:12:28.3788000000025''2015-03-24 00:00:00.00017:10:002015-03-24 00:00:00.00018:47:002015-03-24 00:00:00.00017:16:00NaNPT110507310.001.350.01.351.35000010NaNNaN122.0NegligenteEntre Douro e MinhoTâmega
22015DM4152932015030021973FlorestalBragaVila VerdeLageRUA PROF ABEL MADEIRA3132317216251720941:37:25.5539999999957''8:28:1.8084000000006''2015-03-24 00:00:00.00019:15:002015-03-24 00:00:00.00020:00:002015-03-24 00:00:00.00019:25:00NaNPT110213230.260.000.00.260.26000100NaNNaN60.0DesconhecidaEntre Douro e MinhoBaixo Minho
32015DM3152612015160007563Falso AlarmeViana do CasteloPonte de LimaBoalhosaJ. DEPOSITOS DE AGUA (BOALHOSA)16070917129152957741:44:6.30240000000185''8:28:41.6172000000014''2015-03-24 00:00:00.00020:05:002015-03-24 00:00:00.00021:30:002015-03-24 00:00:00.00020:20:00NaNPT110107090.000.000.00.000.00001000NaNNaN630.0DesconhecidaEntre Douro e MinhoAlto Minho
42015BL1153212015180015874AgrícolaViseuCastro DaireCastro DaireFarejinhas - Bº das Queirós18030421729943699940:54:6.67440000000056''7:55:40.1268000000016''2015-03-24 00:00:00.00020:10:002015-03-24 00:00:00.00021:00:002015-03-24 00:00:00.00020:15:00NaNPT120503040.000.050.00.050.05000100NaNNaN630.0DesconhecidaBeira LitoralDão Lafões
52015DM4152892015030021952FlorestalBragaBarcelosPerelhalERMIDA3026015273350655341:31:36.8795999999941''8:41:58.0343999999977''2015-03-24 00:00:00.00018:03:002015-03-25 00:00:00.00001:50:002015-03-24 00:00:00.00018:15:00NaNPT110202601.500.000.01.501.50100010NaNNaN711.0ReacendimentoEntre Douro e MinhoBaixo Minho
62015TM1153822015170006397FlorestalVila RealBoticasCerdedoVale Chã (CM1032 23, 5470, Portugal)1702062183155186611900-01-01 17:38:0707:54:432015-03-24 00:00:00.00021:40:002015-03-25 00:00:00.00005:45:002015-03-24 00:00:00.00022:00:00NaNPT110802060.5038.000.038.5038.50000010NaNNaN125.0NegligenteTrás-os-MontesBarroso e Padrela
72015BL215922015060013192Falso AlarmeCoimbraPampilhosa da SerraPampilhosa da SerraSOEIRINHO6120621260034720040:5:35.7468000000011''7:59:7.30320000000106''2015-03-25 00:00:00.00010:30:002015-03-25 00:00:00.00011:10:002015-03-25 00:00:00.00010:40:00NaNPT120412060.000.000.00.000.00001000Pampilhosa da SerraNaN60.0DesconhecidaBeira LitoralPinhal Interior Norte
82015DM2153342015130044169FlorestalPortoMatosinhosSanta Cruz do BispoMONTE DA MINA13080815483347063941:12:13.2479999999947''8:40:17.8248000000018''2015-03-25 00:00:00.00015:30:002015-03-25 00:00:00.00018:25:002015-03-25 00:00:00.00015:40:00NaNPT110408080.000.050.00.050.05000100NaNNaNNaNNaNEntre Douro e MinhoAMP e Entre Douro e Vouga
92015TM1153852015170006446FlorestalVila RealMontalegreGralhasGRALHAS17061223569954250041:51:4.15079999998738''7:42:11.7215999999988''2015-03-25 00:00:00.00016:00:002015-03-25 00:00:00.00017:00:002015-03-25 00:00:00.00016:14:00NaNPT110806120.000.010.00.010.01000100NaNNaN125.0NegligenteTrás-os-MontesBarroso e Padrela

Last rows

AnoCodigo SGIFCodigo_ANPCTipoDistritoConcelhoFreguesiaLocalINExylatlonDataAlertaHoraAlertaDataExtincaoHoraExtincaoData1IntervencaoHora1IntervencaoFonteAlertaNUTAA_Povoamento (ha)AA_Mato (ha)AA_Agricola (ha)AA_EspacosFlorestais (pov+mato)(ha)AA_Total (pov+mato+agric) (ha)ReacendimentosQueimadaFalso AlarmeFogachoIncendioAgricolaPerimetroAPSCausaTipoCausaRegião PROFUGF
231652015DM41516252015030056679FlorestalBragaVIZELABarrosas_Santa EulaliaL. DA TORRE3140118626848710241:21:10.7531999999958''8:17:49.7904000000015''2015-08-08 00:00:00.00010:10:002015-08-08 00:00:00.00011:40:002015-08-08 00:00:00.00010:16:00NaNPT110314010.00000.15000.00.15000.1500000100NaNNaNNaNNaNEntre Douro e MinhoBaixo Minho
231662015BL31511962015010055534FlorestalAveiroAveiroCaciaRua dos Ervideiros - Taboeira1050216130941330740:41:15.7452000000114''8:35:27.380400000002''2015-08-08 00:00:00.00013:26:002015-08-08 00:00:00.00015:05:002015-08-08 00:00:00.00013:29:00NaNPT120105020.04360.00000.00.04360.0436000100NaNNaN610.0DesconhecidaBeira LitoralCentro Litoral
231672015DM21531212015130115491FlorestalPortoAmaranteTelõesTODEIA13013520270448255441:18:44.2872000000091''8:6:2.90880000000243''2015-08-08 00:00:00.00013:47:002015-08-08 00:00:00.00017:00:002015-08-08 00:00:00.00014:03:00NaNPT110501350.00000.07000.00.07000.0700100100NaNNaN711.0ReacendimentoEntre Douro e MinhoTâmega
231682015DM41516312015030056725FlorestalBragaCelorico de BastoGémeosL. VINHAÇA3051221069249203341:23:50.8127999999897''8:0:18.7380000000005''2015-08-08 00:00:00.00013:51:002015-08-08 00:00:00.00015:35:002015-08-08 00:00:00.00014:10:00NaNPT110505120.00000.04000.00.04000.0400000100NaNNaN448.0IntencionalEntre Douro e MinhoTâmega
231692015RO1158922015140044046FlorestalSantarémOurémNossa Senhora MisericórdiasLagoa do Furadouro14211116224329408739:36:51.2819999999886''8:34:21.5868000000013''2015-08-08 00:00:00.00014:16:002015-08-08 00:00:00.00015:18:002015-08-08 00:00:00.00014:34:00NaNPT130421110.00000.34250.00.34250.3425000100NaNNaN448.0IntencionalRibatejo e OesteRibatejo
231702015DM41516712015030056919FlorestalBragaFafeFafeRUA DE FORNELO3070919647849782641:26:58.6212000000126''8:10:30.8603999999981''2015-08-09 00:00:00.00002:10:002015-08-09 00:00:00.00003:15:002015-08-09 00:00:00.00002:20:00NaNPT110307090.00000.50000.00.50000.5000000100NaNNaN60.0DesconhecidaEntre Douro e MinhoBaixo Minho
231712015BL11510182015180041997FlorestalViseuCinfãesCinfãesVentuzela18040320311045880041:5:53.9843999999988''8:5:46.4352000000019''2015-08-09 00:00:00.00004:40:002015-08-09 00:00:00.00009:30:002015-08-09 00:00:00.00004:53:00NaNPT110504030.00002.50000.02.50002.5000000010NaNNaN125.0NegligenteEntre Douro e MinhoTâmega
231722015BL31512272015010055691Falso AlarmeAveiroSanta Maria da FeiraFeiraavenida 25 de abril1090616439543969840:55:31.7999999999984''8:33:21.0132000000013''2015-08-09 00:00:00.00005:50:002015-08-09 00:00:00.00006:40:002015-08-09 00:00:00.00005:55:00NaNPT110609060.00000.00000.00.00000.0000001000NaNNaNNaNNaNEntre Douro e MinhoAMP e Entre Douro e Vouga
231732015DM21532262015130115988FlorestalPortoPaços de FerreiraPaços de FerreiraLugar alto das Cavadas13091218066647856941:16:33.7691999999942''8:21:50.2019999999975''2015-08-09 00:00:00.00008:20:002015-08-09 00:00:00.00010:40:002015-08-09 00:00:00.00008:25:00NaNPT110509120.05000.00000.00.05000.0500100100NaNNaN711.0ReacendimentoEntre Douro e MinhoTâmega
231742015BL31512292015010055723Falso AlarmeAveiroSanta Maria da FeiraRio MeãoCasais de Baixo1092116179244310540:57:21.8375999999995''8:35:12.6744000000024''2015-08-09 00:00:00.00009:09:002015-08-09 00:00:00.00011:03:002015-08-09 00:00:00.00009:12:00NaNPT110609210.00000.00000.00.00000.0000001000NaNNaNNaNNaNEntre Douro e MinhoAMP e Entre Douro e Vouga